针对稀疏投影和动态运动，提出几何感知的高斯场重建方法
TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction
- 用多分辨率哈希编码捕捉局部空间先验，约束稀疏条件下的参数
- 在真实数据上达到当前最优重建精度，稀疏视图下误差降低27%
- 适合医学断层成像中静态与呼吸运动场景的高保真重建
3D高斯点阵（3DGS）以高效高质量革新了三维场景表示。尽管近期在计算机断层扫描（CT）中的应用已显潜力，但在高度稀疏视角投影和动态运动下仍存在严重伪影。为此，我们提出用于断层重建的几何感知高斯场（TG-Field），该框架适用于静态与动态场景。采用多分辨率哈希编码捕获局部空间先验，在超稀疏条件下正则化原始参数。通过引入时间条件表示与时空注意力模块，自适应聚合特征，解决时空模糊性并保证时间一致性；同时设计运动流网络建模细粒度呼吸运动，追踪局部解剖形变。在合成与真实数据集上的大量实验表明，TG-Field持续优于现有方法，在高度稀疏视图下实现当前最优重建精度。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation with superior efficiency and quality. While recent adaptations for computed tomography (CT) show promise, they struggle with severe artifacts under highly sparse-view projections and dynamic motions. To address these challenges, we propose Tomographic Geometry Field (TG-Field), a geometry-aware Gaussian deformation framework tailored for both static and dynamic CT reconstruction. A multi-resolution hash encoder is employed to capture local spatial priors, regularizing primitive parameters under ultra-sparse settings. We further extend the framework to dynamic reconstruction by introducing time-conditioned representations and a spatiotemporal attention block to adaptively aggregate features, thereby resolving spatiotemporal ambiguities and enforcing temporal coherence. In addition, a motion-flow network models fine-grained respiratory motion to track local anatomical deformations. Extensive experiments on synthetic and real-world datasets demonstrate that TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.
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